




"""simple docstring"""


from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)






@flax.struct.dataclass
class    lowercase:







 '''simple docstring'''





 lowercase__					   =   42

 # setable values
 lowercase__					   =   42
 lowercase__					   =   42
 lowercase__					   =   None


 @classmethod
 def 					UpperCamelCase_  (							cls:						List[Any], a_:						Union[str, Any], a_:						Tuple, a_:						Union[str, Any]						):




   '''simple docstring'''



   return cls(common=_UpperCAmelCase, init_noise_sigma=_UpperCAmelCase, timesteps=_UpperCAmelCase						)







@dataclass
class    lowercase(	snake_case__					):







 '''simple docstring'''





 lowercase__					   =   42







class    lowercase(	snake_case__			,     snake_case__					):







 '''simple docstring'''





 lowercase__					   =   [e.name for e in FlaxKarrasDiffusionSchedulers]

 lowercase__					   =   42


 @property
 def 					UpperCamelCase_  (							self:						Union[str, Any]						):




   '''simple docstring'''



   return True


 @register_to_config
 def __init__(							self:						Any, a_:						Optional[int] = 1_000, a_:						Tuple = 0.0_001, a_:						Optional[Any] = 0.02, a_:						int = "linear", a_:						Tuple = None, a_:						List[Any] = "fixed_small", a_:						List[Any] = True, a_:						Any = "epsilon", a_:						str = jnp.floataa, ):




   '''simple docstring'''



   _snake_case				:						Any								=    dtype


 def 					UpperCamelCase_  (							self:						int, a_:						List[str] = None						):




   '''simple docstring'''



   if common is None:
     _snake_case				:						Optional[int]								=    CommonSchedulerState.create(self						)

   # standard deviation of the initial noise distribution
   _snake_case				:						Optional[int]								=    jnp.array(1.0, dtype=self.dtype						)

   _snake_case				:						str								=    jnp.arange(0, self.config.num_train_timesteps						).round()[::-1]

   return DDPMSchedulerState.create(
       common=_UpperCAmelCase, init_noise_sigma=_UpperCAmelCase, timesteps=_UpperCAmelCase, )


 def 					UpperCamelCase_  (							self:						Any, a_:						Optional[Any], a_:						Union[str, Any], a_:						Tuple = None						):




   '''simple docstring'''



   return sample


 def 					UpperCamelCase_  (							self:						Optional[int], a_:						Union[str, Any], a_:						Any, a_:						List[Any] = ()						):




   '''simple docstring'''



   _snake_case				:						Optional[int]								=    self.config.num_train_timesteps // num_inference_steps
   # creates integer timesteps by multiplying by ratio
   # rounding to avoid issues when num_inference_step is power of 3
   _snake_case				:						Tuple								=    (jnp.arange(0, _UpperCAmelCase						) * step_ratio).round()[::-1]

   return state.replace(
       num_inference_steps=_UpperCAmelCase, timesteps=_UpperCAmelCase, )


 def 					UpperCamelCase_  (							self:						List[str], a_:						Tuple, a_:						Dict, a_:						List[str]=None, a_:						Any=None						):




   '''simple docstring'''



   _snake_case				:						Optional[int]								=    state.common.alphas_cumprod[t]
   _snake_case				:						Union[str, Any]								=    jnp.where(t > 0, state.common.alphas_cumprod[t - 1], jnp.array(1.0, dtype=self.dtype						)						)

   # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
   # and sample from it to get previous sample
   # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
   _snake_case				:						List[str]								=    (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

   if variance_type is None:
     _snake_case				:						Tuple								=    self.config.variance_type

   # hacks - were probably added for training stability
   if variance_type == "fixed_small":
     _snake_case				:						List[Any]								=    jnp.clip(_UpperCAmelCase, a_min=1E-20						)
   # for rl-diffuser https://arxiv.org/abs/2205.09991
   elif variance_type == "fixed_small_log":
     _snake_case				:						int								=    jnp.log(jnp.clip(_UpperCAmelCase, a_min=1E-20						)						)
   elif variance_type == "fixed_large":
     _snake_case				:						Union[str, Any]								=    state.common.betas[t]
   elif variance_type == "fixed_large_log":
     # Glide max_log
     _snake_case				:						Dict								=    jnp.log(state.common.betas[t]						)
   elif variance_type == "learned":
     return predicted_variance
   elif variance_type == "learned_range":
     _snake_case				:						List[Any]								=    variance
     _snake_case				:						int								=    state.common.betas[t]
     _snake_case				:						Any								=    (predicted_variance + 1) / 2
     _snake_case				:						int								=    frac * max_log + (1 - frac) * min_log

   return variance


 def 					UpperCamelCase_  (							self:						Any, a_:						int, a_:						Dict, a_:						Tuple, a_:						Dict, a_:						Dict = None, a_:						Any = True, ):




   '''simple docstring'''



   _snake_case				:						int								=    timestep

   if key is None:
     _snake_case				:						int								=    jax.random.PRNGKey(0						)

   if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
     _snake_case						,   _snake_case				:						List[Any]								=    jnp.split(_UpperCAmelCase, sample.shape[1], axis=1						)
   else:
     _snake_case				:						Optional[Any]								=    None

   # 1. compute alphas, betas
   _snake_case				:						str								=    state.common.alphas_cumprod[t]
   _snake_case				:						Tuple								=    jnp.where(t > 0, state.common.alphas_cumprod[t - 1], jnp.array(1.0, dtype=self.dtype						)						)
   _snake_case				:						Dict								=    1 - alpha_prod_t
   _snake_case				:						Dict								=    1 - alpha_prod_t_prev

   # 2. compute predicted original sample from predicted noise also called
   # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
   if self.config.prediction_type == "epsilon":
     _snake_case				:						List[Any]								=    (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
   elif self.config.prediction_type == "sample":
     _snake_case				:						Union[str, Any]								=    model_output
   elif self.config.prediction_type == "v_prediction":
     _snake_case				:						Any								=    (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
   else:
     raise ValueError(
         f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` "
         """ for the FlaxDDPMScheduler."""						)

   # 3. Clip "predicted x_0"
   if self.config.clip_sample:
     _snake_case				:						Optional[int]								=    jnp.clip(_UpperCAmelCase, -1, 1						)

   # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
   # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
   _snake_case				:						int								=    (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
   _snake_case				:						Optional[int]								=    state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

   # 5. Compute predicted previous sample µ_t
   # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
   _snake_case				:						Any								=    pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

   # 6. Add noise
   def random_variance():
     _snake_case				:						Optional[Any]								=    jax.random.split(_UpperCAmelCase, num=1						)
     _snake_case				:						str								=    jax.random.normal(_UpperCAmelCase, shape=model_output.shape, dtype=self.dtype						)
     return (self._get_variance(_UpperCAmelCase, _UpperCAmelCase, predicted_variance=_UpperCAmelCase						) ** 0.5) * noise

   _snake_case				:						List[Any]								=    jnp.where(t > 0, random_variance(), jnp.zeros(model_output.shape, dtype=self.dtype						)						)

   _snake_case				:						Optional[int]								=    pred_prev_sample + variance

   if not return_dict:
     return (pred_prev_sample, state)

   return FlaxDDPMSchedulerOutput(prev_sample=_UpperCAmelCase, state=_UpperCAmelCase						)


 def 					UpperCamelCase_  (							self:						str, a_:						List[str], a_:						Optional[Any], a_:						Optional[Any], a_:						List[str], ):




   '''simple docstring'''



   return add_noise_common(state.common, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase						)


 def 					UpperCamelCase_  (							self:						List[str], a_:						Optional[int], a_:						Optional[int], a_:						Optional[Any], a_:						Optional[Any], ):




   '''simple docstring'''



   return get_velocity_common(state.common, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase						)


 def __len__(							self:						Optional[int]						):




   '''simple docstring'''



   return self.config.num_train_timesteps







'''simple docstring'''

from __future__ import annotations






def 				a  (     UpperCamelCase_    :    list[int]      ,					UpperCamelCase_    :    list[int]      ,					UpperCamelCase_    :    int			)       ->	tuple[float, list[float]]:
		snake_case__	     =list(range(len(UpperCamelCase_			)			)			)
		snake_case__	     =[v / w for v, w in zip(UpperCamelCase_      ,					UpperCamelCase_			)]
		index.sort(key=lambda UpperCamelCase_			: ratio[i]      ,					reverse=UpperCamelCase_			)

		snake_case__	     =0
		snake_case__	     =[0] * len(UpperCamelCase_			)
		for i in index:
				if weight[i] <= capacity:
						snake_case__	     =1
						max_value += value[i]
						capacity -= weight[i]
				else:
						snake_case__	     =capacity / weight[i]
						max_value += value[i] * capacity / weight[i]
						break

		return max_value, fractions


if __name__ == "__main__":
			import doctest

			doctest.testmod()




